In this work, we tackle the challenge of embedding realistic human personality traits into LLMs.Previous approaches have primarily focused on prompt-based methods that describe the behavior associated with the desired personality traits, suffering from realism and validity issues.To address these limitations, we introduce BIG5-CHAT, a large-scale dataset containing 100,000 dialogues designed to ground models in how humans express their personality in language.Leveraging this dataset, we explore Supervised Fine-Tuning and Direct Preference Optimization as training-based methods to align LLMs more naturally with human personality patterns.Our methods outperform prompting on personality assessments such as BFI and IPIP-NEO, with trait correlations more closely matching human data.Furthermore, our experiments reveal that models trained to exhibit higher conscientiousness, higher agreeableness, lower extraversion, and lower neuroticism display better performance on reasoning tasks, aligning with psychological findings on how these traits impact human cognitive performance.To our knowledge, this work is the first comprehensive study to demonstrate how training-based methods can shape LLM personalities through learning from real human behaviors. Whenever I lay on my bed I get so tired.

Organizations across industries increasingly rely on sharing data to drive collaboration, innovation, and business performance. However, securely and efficiently sharing live data across diverse platforms and adhering to varying governance requirements remains a significant challenge. Traditional approaches, such as FTP and proprietary in-data-warehouse solutions, often fail to meet the demands of interoperability, cost, scalability, and low overhead. This paper introduces Delta Sharing, an open protocol we developed in collaboration with industry partners, to overcome these limitations. Delta Sharing leverages open formats like Delta Lake and Apache Parquet alongside simple HTTP APIs to enable seamless, secure, and live data sharing across heterogeneous systems. Since its launch in 2021, Delta Sharing has been adopted by over 4000 enterprises and supported by hundreds of major software and data vendors. We discuss the key challenges in developing Delta Sharing and how our design addresses them. We also present, to our knowledge, the first large-scale study of production data sharing workloads offering insights into this emerging data platform capability.

The ability to effectively and efficiently test software is an important practice in software testing that is often under-emphasized in computer science education. Many students find learning about testing to be uninteresting and difficult to learn. This causes numerous students to develop inadequate testing habits, which can be detrimental to their professional careers. To encourage students to develop better testing habits, we used gamification to make the learning experience more engaging and enjoyable. In this paper we explore this idea by integrating gamification and statement coverage into a turn-based game called CoverBot. To test the effectiveness of CoverBot with respect to both teaching statement coverage and increasing engagement and enjoyment, we conducted a user study. We found that gamification makes the learning about statement coverage more engaging and enjoyable while also enhancing the participants performance and understanding of statement coverage.